70 citations · 84 across the 13 of their papers we have counts for
13 papers
Compressed Smooth Sparse Decomposition
Shancong Mou, Jianjun Shi
Image-based anomaly detection systems are of vital importance in various manufacturing applications. The resolution and acquisition rate of such systems is increasing significantly…
An Augmented Regression Model for Tensors with Missing Values
Feng Wang, Mostafa Reisi Gahrooei, Zhen Zhong +2
Heterogeneous but complementary sources of data provide an unprecedented opportunity for developing accurate statistical models of systems. Although the existing methods have shown…
Additive Tensor Decomposition Considering Structural Data Information
Shancong Mou, Andi Wang, Chuck Zhang +1
Tensor data with rich structural information becomes increasingly important in process modeling, monitoring, and diagnosis. Here structural information is referred to structural pr…
Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage
Xiaowei Yue, Yuchen Wen, Jeffrey H. Hunt +1
In the machine learning domain, active learning is an iterative data selection algorithm for maximizing information acquisition and improving model performance with limited trainin…
Real-time Data-driven Quality Assessment for Continuous Manufacturing of Carbon Nanotube Buckypaper
Xinran Shi, Xiaowei Yue, Zhiyong Liang +1
Carbon nanotube (CNT) thin sheet, or buckypaper, has shown great potential as a multifunctional platform material due to its desirable properties, including its lightweight nature,…
Effective Model Calibration via Sensible Variable Identification and Adjustment, with Application to Composite Fuselage Simulation
Yan Wang, Xiaowei Yue, Rui Tuo +2
Estimation of model parameters of computer simulators, also known as calibration, is an important topic in many engineering applications. In this paper, we consider the calibration…
AKMD : An Adaptive Framework for Online Sensing and Anomaly Quantification
Hao Yan, Kamran Paynabar, Jianjun Shi
In point-based sensing systems such as coordinate measuring machines (CMM) and laser ultrasonics where complete sensing is impractical due to the high sensing time and cost, adapti…
Learning the Treatment Effects on FTIR Signals Subject to Multiple Sources of Uncertainties
Hongzhen Tian, Andi Wang, Jialei Chen +5
Fourier-transform infrared spectroscopy (FTIR) is a versatile technique for characterizing the chemical composition of the various uncertainties, including baseline shift and multi…
Robustness and Tractability for Non-convex M-estimators
Ruizhi Zhang, Yajun Mei, Jianjun Shi +1
We investigate two important properties of M-estimator, namely, robustness and tractability, in linear regression setting, when the observations are contaminated by some arbitrary…
High Dimensional Process Monitoring Using Robust Sparse Probabilistic Principal Component Analysis
Mohammad Nabhan, Yajun Mei, Jianjun Shi
High dimensional data has introduced challenges that are difficult to address when attempting to implement classical approaches of statistical process control. This has made it a t…
A novel approach for fusion of heterogeneous sources of data
Mostafa Reisi Gahrooei, Hao Yan, Kamran Paynabar +1
With advancements in sensor technology, a heterogeneous set of data, containing samples of scalar, waveform signal, image, or even structured point cloud are becoming increasingly…
Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning
Chen Zhang, Hao Yan, Seungho Lee +1
Multivariate functional data from a complex system are naturally high-dimensional and have complex cross-correlation structure. The complexity of data structure can be observed as…
Tensor Mixed Effects Model with Applications in Nanomanufacturing Inspection
Xiaowei Yue, Jin Gyu Park, Zhiyong Liang +1
Raman mapping technique has been used to perform in-line quality inspections of nanomanufacturing processes. In such an application, massive high-dimensional Raman mapping data wit…